# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""
##############test textcnn example on movie review#################
python eval.py
"""
import os

import mindspore.nn as nn
from mindspore.nn.metrics import Accuracy
from mindspore import context
from mindspore.train.model import Model
from mindspore.train.serialization import load_checkpoint, load_param_into_net

from model_utils.config import config
from src.DPCNN import DPCNN
from src.dataset import MovieReview

def eval_net():
    '''eval net'''
    instance = MovieReview(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
    context.set_context(device_id=int(os.getenv('DEVICE_ID', '0')))
    dataset = instance.create_test_dataset(batch_size=config.batch_size)
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
    net = DPCNN(vocab_len=instance.get_dict_len(), word_len=config.word_len, num_classes=config.num_classes, vec_length=config.vec_length, num_filters=250)
    opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate=0.001,
                  weight_decay=float(config.weight_decay))

    print(config.checkpoint_file_path)
    
    param_dict = load_checkpoint(config.checkpoint_file_path)
    print("load checkpoint from [{}].".format(config.checkpoint_file_path))

    load_param_into_net(net, param_dict)
    net.set_train(False)
    model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc': Accuracy()})

    acc = model.eval(dataset)
    print("DPCNN accuracy: ", acc)

if __name__ == '__main__':
    eval_net()
